AI for Medicine

AI for Medicine is a three-course Specialization that equips learners with practical experience in applying machine learning to solve real-world medical challenges. The courses delve into diagnosing diseases from medical images, predicting patient survival rates, estimating treatment effects, and automating medical dataset labeling using natural language processing. Throughout the program, participants will gain insights into the nuances of applying AI to medical use cases and develop a deeper understanding of neural networks.

• Course 1 focuses on creating convolutional neural network image classification and segmentation models for diagnosing lung and brain disorders.
• Course 2 covers building risk models and survival estimators for heart disease using statistical methods and a random forest predictor.
• Course 3 delves into building a treatment effect predictor, applying model interpretation techniques, and utilizing natural language processing to extract information from radiology reports.

This Specialization is suitable for individuals with some understanding of the math and coding behind AI algorithms. While no prior medical expertise is required, a working knowledge of deep neural networks and proficiency in Python programming at an intermediate level are essential. Learners new to machine learning or neural networks are recommended to first take the Deep Learning Specialization offered by deeplearning.ai and taught by Andrew Ng.